The second category comprises an ablation, called
a
“conditional neural grammar” (CNG), that is identical to our Nsg model but is trained without any of the attribute information. In other words, the CNG model is trained only on the program syntax.
The third category includes GNN2NAG [1]}, a graph-neural-network-based method that uses an attribute grammar but learns the attributes from data rather than computing them symbolically. See Appendix  for more details on the baselines.
